1 10 6 https://repository.horizon.ac.id/files/original/f9185c98c442777fc2ba9510c59b274c.pdf 52bfdc8364c4cc10a131fb51d34da9b8 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 14 NO. 1 (2025) MARCH 2025 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Detailed Analyses and Efficient Identification of Malware Evidence in CLaMP Dataset based on Machine Learning Approaches Subject The topic of the resource Malware Identification; Machine Learning Algorithms; Feature Selection; Windows PE headers Description An account of the resource Malware is a malicious software that is used to launch attacks of different types in computer networks and cyber space. Several signature and machine learning-based approaches have been used for the identification of malware types in the past. However,signature-based detection approaches have been reported to have serious limitations which gave room for machine learning-based malware identification techniques to be more popular. Despite the promises of the ML methods in the identification of malware evidence, some of the ML approaches in literature have poor detection rates which can be as a result of the size and nature of the patterns in the datasets used. This study used a dataset named CLaMP for the training and testing of the malware classification models. Firstly, comprehensive exploratory analyses of the dataset were carried out with a view to understanding the data distributions in it better and make informative decisions on how to pre-process and apply it for malware identification. During the experimentations, two scenarios were established before feeding the data into the learning algorithms. Scenario 1 involves building malware identification model without data cleaning and feature selection while scenario 2 involves the cleaning of the data and selection of promising features for building the models.In scenario 2, Recursive Feature Elimination (RFE) technique was used for selecting the promising attributes which were used to build the two malware classification models. Naive Bayes (NB) and Logistic Regression (LR) algorithms were used for building the models. The hyper parameters of the two selected algorithms were varied and the models tested and validated severally before optimal performances were arrived at. The results of the models were compared based on the selected metrics, namely: accuracy, precision, recall, f1-score and Area Under the Curve (AUC). Experimental results showed that in the scenario 1, where the dataset was not pre-processed and all the attributes were used for the model building, poor results were obtained by both models in all metrics except in recall. However, NB-based malware identification model slightly performed better than LR in all the metrics. It was also discovered that both NB and LR-based malware identification models performed well in scenario 2 when the dataset was pre-processed and promising features were selected using RFE. This study concluded that the detailed exploratory analyses, data cleaning and feature subset selection methods helped in achieving promising results from the malware identification models Creator An entity primarily responsible for making the resource 1M. O. Ayinla, 2A. M. Oyelakin, 3U. A. Adeniyi, 4K. O. Tajudeen, 5O. J. Olaleye, Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource March 2025 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/b3250de8ff6aa1e36f2052c58a7ec85f.pdf 5704c68019e6e33263ceec84cf7e1bd3 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 14 NO. 1 (2025) MARCH 2025 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An Android-Based Smart RSU Framework for Enhanced Urban Traffic Management Subject The topic of the resource Roadside Units (RSUs), Traffic Monitoring, YOLOv8, SAHI Algorithm, Android-Based RSUs, Real-Time Object Detection, Urban Traffic Management. Description An account of the resource This paper addresses critical challenges in the deployment and effectiveness of traditional Roadside Units (RSUs) in traffic monitoring systems and proposes a novel, cost-effective approach using Android-based smart RSUs. Leveraging mobile phone architecture, YOLOv8 , the SAHI algorithm and chat gpt-4o, the system provides real-time traffic data collection, vehicle detection, and congestion analysis. This paper evaluates the performance of different cost tiers of mobile devices, discusses traditional traffic monitoring challenges, and identifies key gaps in current RSU technologies. The proposed system offers enhanced scalability, flexibility, and reduced cost, making it an ideal solution for urban traffic management. Creator An entity primarily responsible for making the resource Mohamed Fadhil, Qutaiba Ibrahim Ali Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource March 2025 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/4734239be5c6573bf0fb8ec6ff108711.pdf f37f4459b78fa3305b294ac9bb7f68c6 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 14 NO. 1 (2025) MARCH 2025 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource The Evolution of Software Engineering: From Prehistoric Beginnings to the Age of Artificial Intelligence Subject The topic of the resource Artificial Intelligence (AI), AI driven development, Agile Methodology, Machine Learning, software engineering Description An account of the resource This article presents a thorough overview of the historical background of software engineering, tracing its roots from antiquity to the contemporary time dominated by artificial intelligence (AI). Beginning with the basic programming languages of the mid-1900s, the article stresses major milestones, including the birth of object-oriented programming, the expansion of software engineering practices, and the transformational impact of structured programming. Additionally, the paper examines the limitations, possible hazards, ethical considerations, and environmental consequences of AI-powered software engineering, including machine learning (ML), the Agile revolution and its revolutionary impact on software development practices. Through the analysis of this developmental trajectory, the essay offers vital insights into the progressive nature of software engineering and the pivotal role of artificial intelligence in shaping its future. Creator An entity primarily responsible for making the resource Jonah Vincent Joshua, Diosdado Asumu Ndong Andeme Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource March 2025 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/1d36b4c4fd0a023925d75122edeb6a55.pdf c5b5978fd1a64086913e90ae1a82a417 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 14 NO. 1 (2025) MARCH 2025 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource User Rating Prediction Method Based on Fine-tuning of Large Language Models Subject The topic of the resource large language model; fine-tuning; attention mechanism; interpretable prediction Description An account of the resource Online reviews in social networks reflect users' preferences for specific attributes of products. Accurate predictions of user ratings based on these reviews can help businesses better understand genuine user feedback. The purpose of this study is to fine-tune large language models using online reviews and corresponding user rating data, generating a large model for predicting user ratings based on reviews. An attention mechanism is introduced to calculate attention weights for fine-grained review texts, reflecting the contribution of different text features to user rating prediction. By visualizing these weights, the process of calculating the predicted rating values can be explained. Experimental results show that the proposed interpretable user rating prediction method can effectively visualize the attention weights of important text features in the decision-making process of the large rating prediction model. Compared to the baseline model, the mean absolute error is reduced by 1.96, and the root mean square error is reduced by 1.73. Creator An entity primarily responsible for making the resource Qi Zhang, Hao Zhong Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource March 2025 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/775b54d5369850ca0c1e6d5f9702c19c.pdf af9a872966a55418992c47b4a2e1cd09 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 14 NO. 1 (2025) MARCH 2025 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Centrality Maximization Approach for Link Recommendation Subject The topic of the resource social networks; link recommendation; node centrality; submodular function maximization Description An account of the resource In social networks, the goal of link recommendation is to recommend links for nodes and add them to the network, thereby satisfying the potential link interests of the nodes. The centrality of nodes in social networks typically quantifies the importance of nodes in the network. Some nodes may desire to increase their centrality by adding links. First, a multi-community centrality measurement method is proposed, and based on harmonic centrality, a hybrid centrality measurement method is introduced. Next, the link recommendation problem is regarded as a problem of maximizing node hybrid centrality, which can be formally modeled as a submodular function maximization problem. A greedy algorithm with performance guarantees can be directly applied to select the best links. Compared to existing link prediction and link recommendation algorithms, our algorithm recommends links that better improve the hybrid centrality of users. Creator An entity primarily responsible for making the resource Qi Zhang, Hao Zhong Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource March 2025 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/c445d24f2e6fa5e860b2229fae90c424.pdf 65fa349be25307a9dd7cf34950173043 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 14 NO. 1 (2025) MARCH 2025 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Study on Wearable Tech Interfaces and Perception: Cognitive AI-Enabled Device Subject The topic of the resource Smart glass; virtual avatar; AI-enabled device; GANs; ReRAM Description An account of the resource In our paper, we explore the possibility of utilizing state-of-the-art hardware architecture to develop an interactive Artificial Intelligence(AI) based virtual agent in an Internet of Things (IoT) enabled smart glass. In contrast to the traditional system that relies on Central Processing Unit(CPU) and Graphical Processing Unit(GPU), we examine the possibility of a proposed hardware system that employs neuromorphic computing for precise and energy-efficient processing in real-time, resolving the problems of latency and excessive power consumption. When combined with conventional Complementary Metal-Oxide-Semiconductor (CMOS) technology, Resistive Random Access Memory(ReRAM) offers non-volatile, fast memory that facilitates parallel computing and guarantees smooth data store and retrieval for wearables with limited resources. This study also emphasizes the application of intelligent virtual agents based on cognitive AI in wearable technology. In order to create an immersive human-computer interface, we attempted to create an intelligent interactive AI avatar with Cycle Generative Adversarial Network (CycleGAN) that mimics the user's traits and further pushes it to the large Language Model(LLM) to generate motion sequences that perform additional tasks, transforming LLM prompt reactions into movements. Creator An entity primarily responsible for making the resource 1Debjyoti Bagchi, Samir Biswas Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource March 2025 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text